Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “evolutionary dynamics”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Breaking the Ice: A Review of Phages in Polar Ecosystems

Bacteriophages, or phages, are viruses that infect and replicate within bacterial hosts, playing a significant role in regulating microbial populations and ecosystem dynamics. However, phages from extreme environments such as polar regions remain relatively understudied due to challenges such as restricted ecosystem access and low biomass. In this study, understanding the diversity, structure, and functions of polar phages is crucial for advancing our knowledge of the microbial ecology and biogeochemistry of these environments. In this review, we will explore the current state of knowledge on phages from the Arctic and Antarctic, focusing on insights gained from -omic studies, phage isolation, and virus-like particle abundance data. Metagenomic studies of polar environments have revealed a high diversity of phages with unique genetic characteristics, providing insights into their evolutionary and ecological roles. Phage isolation studies have identified novel phage–host interactions and contributed to the discovery of new phage species. Virus-like particle abundance and lysis rate data, on the other hand, have highlighted the importance of phages in regulating bacterial populations and nutrient cycling in polar environments. Overall, this review aims to provide a comprehensive overview of the current state of knowledge about polar phages, and by synthesizing these different sources of information, we can better understand the diversity, dynamics, and functions of polar phages in the context of ongoing climate change, which will help to predict how polar ecosystems and residing phages may respond to future environmental perturbations.

09 BIOMASS FUELS↗
DOE OSTI2021

Inference of neutrino flavor evolution through data assimilation and neural differential equations

The evolution of neutrino flavor in dense environments such as core-collapse supernovae and binary compact object mergers constitutes an important and unsolved problem. Its solution has potential implications for the dynamics and heavy-element nucleosynthesis in these environments. In this paper, we build upon recent work to explore inference-based techniques for the estimation of model parameters and neutrino flavor evolution histories. Here, we combine data assimilation, ordinary differential equation solvers, and neural networks to craft an inference approach tailored for nonlinear dynamical systems. Using this architecture, and a simple two-neutrino-beam, two-flavor model, we compare the performances of nine different optimization algorithms and expand upon previous assessments of the efficacy of inference for tackling problems in flavor evolution. We find that employing this new architecture, together with evolutionary optimization algorithms, accurately captures flavor histories in the small-scale model and allows us to quickly explore both model parameters and initial flavor content. In future work we plan to extend these inference techniques to large numbers of neutrinos.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Finding Your Niche: An Evolutionary Approach to HPC Topologies

Traditional interconnection network design approaches focus on building general network topologies by optimizing the bisection bandwidth or minimizing the network’s diameter to reduce the maximum distance between any two nodes, thus amortizing the overall execution time of the HPC workloads. While such network topologies may accommodate a wide variety of applications in general, this may result in sub-optimal performance for many frequently-executed or dynamic workloads. In this paper, instead of focusing on designing an all-encompassing, general-purpose network topology, we develop a methodology to design customized network interconnects, evolved by “finding” the optimal topologies for a particular target workload given by its communication and contention profiles. To this end, we implement a Genetic Algorithm (GA)-based approach for network topology design tailored to improve the overall execution time of a particular workload of interest. We conducted extensive experiments with well-known motifs in physics-based workloads (Sweep3D and FFT), as well as with a representative graph application (MiniVite), using the well-known Structural Simulation Toolkit (SST) Macroscale Element Library (SST/macro) simulator for network interconnect evaluation. We demonstrate that our genetic algorithm-based approach is robust enough to find the underlying optimal topology of a particular workload.

network interconnects, graph search, meta-heuristi↗
DOE OSTI2025

Comprehensive Analysis of the Relative Dispersion of Droplet-Size Distributions and Their Relationships to Key Physical Fog Processes Under Different Aerosol Conditions and Evolutionary Stages

The relative dispersion of cloud and fog droplets has significant impacts on aerosol indirect effects, radiative transfer, and microphysical processes. However, previous studies have been mostly concerned with clouds, with limited studies on fog, particularly those that examine the combined influences of all key physical processes and their roles during fog evolution. As such, this study aims to conduct a comprehensive investigation by examining the relationships between relative dispersion and other microphysical variables, as well as the underlying microphysical and dynamic processes, based on field fog campaigns in polluted and clean conditions. In polluted fog, droplet concentrations are higher, leading to smaller droplets and increased dispersion. The correlation between dispersion and droplet volume-mean radius is positive in the polluted fog, but shifts to negative in clean fog. Here, we attribute the difference to various microphysical processes like aerosol activation, condensation, collision-coalescence, and entrainment-mixing. In polluted fog, high aerosol concentrations, low supersaturations, and strong turbulence (entrainment-mixing) provide suitable conditions for the simultaneous occurrence of droplet condensation and aerosol activation, resulting in a positive correlation between dispersion and volume-mean radius, especially during the fog formation stage. In contrast, during the mature stage in clean fog, condensation is dominant with weak aerosol activation leading to a negative correlation between relative dispersion and volume-mean radius. The collision-coalescence process is more active in the mature stage, increasing radii and leading to the negative correlation between dispersion and volume-mean radius. This result sheds new light on understanding the relative dispersion and mechanisms in fog under different aerosol backgrounds.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Is Betelgeuse the Outcome of a Past Merger?

We explore the possibility that the star α Orionis (Betelgeuse) is the outcome of a merger that occurred in a low-mass-ratio (q= $\mathcal{M}$ 2 /$\mathcal{M}$ 1 = 0.07–0.25) binary system some time in the past hundreds of thousands of years. To that goal, we present a simple analytical model to approximate the perturbed internal structure of a post-merger object following the coalescence of a secondary in the mass range 1–4 M ⊙ into the envelope of a 15–17 M ⊙ primary. We then compute the long-term evolution of post-merger objects for a grid of initial conditions and make predictions about their surface properties for evolutionary stages that are consistent with the observed location of Betelgeuse in the Hertzsprung–Russell diagram. We find that if a merger occurred after the end of the primary’s main-sequence phase, while it was expanding toward becoming a red supergiant star and typically with radius ∼200–300 R ⊙ , then its envelope is spun up to values that remain in a range consistent with Betelgeuse observations for thousands of years of evolution. We argue that the best scenario that can explain both the fast rotation of Betelgeuse and its observed large space velocity is one where a binary was dynamically ejected by its parent cluster a few million years ago and then subsequently merged. An alternative scenario in which the progenitor of Betelgeuse was spun up by accretion in a binary and released by the supernova explosion of the companion requires a finely tuned set of conditions but cannot be ruled out.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Generative $β$-hairpin design using a residue-based physicochemical property landscape

De novo peptide design is a new frontier that has broad application potential in the biological and biomedical fields. Most existing models for de novo peptide design are largely based on sequence homology that can be restricted based on evolutionarily derived protein sequences and lack the physicochemical context essential in protein folding. Generative machine learning for de novo peptide design is a promising way to synthesize theoretical data that are based on, but unique from, the observable universe. In this study, we created and tested a custom peptide generative adversarial network intended to design peptide sequences that can fold into the -hairpin secondary structure. This deep neural network model is designed to establish a preliminary foundation of the generative approach based on physicochemical and conformational properties of 20 canonical amino acids, for example, hydrophobicity and residue volume, using extant structure-specific sequence data from the PDB. The beta generative adversarial network model robustly distinguishes secondary structures of hairpin from α helix and intrinsically disordered peptides with an accuracy of up to 96% and generates artificial -hairpin peptide sequences with minimum sequence identities around 31% and 50% when compared against the current NCBI PDB and nonredundant databases, respectively. These results highlight the potential of generative models specifically anchored by physicochemical and conformational property features of amino acids to expand the sequence-to-structure landscape of proteins beyond evolutionary limits.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Microbial Evolution Drives Adaptation of Substrate Degradation on Decadal to Centennial Time Scales Relevant to Global Change

ABSTRACT Understanding microbial adaptation is crucial for predicting how soil carbon dynamics and global biogeochemical cycles will respond to climate change. This study employs the DEMENT model of microbial decomposition, along with empirical mutation and dispersal rates, to explore the roles of mutation and dispersal in the adaptation of soil microbial populations to shifts in litter chemistry, changes that are anticipated with climate‐driven vegetation dynamics. Following a change in litter chemistry, mutation generally allows for a higher rate of litter decomposition than dispersal, especially when dispersal predominantly introduces genotypes already present in the population. These findings challenge the common idea that mutation rates are too low to affect ecosystem processes on ecological timescales. These results demonstrate that evolutionary processes, such as mutation, can help maintain ecosystem functioning as the climate changes.

Abs, Elsa↗
DOE OSTI2023

Multiobjective Hyperparameter Optimization for Deep Learning Interatomic Potential Training Using NSGA-II

Deep neural network (DNN) potentials are an emerging tool for simulation of dynamical atomistic systems, with the promise of quantum mechanical accuracy at speedups of 10000$\times$. As with other DNN methods, hyperparameters used during training can make a substantial difference in model accuracy, and optimal settings vary with dataset. To enable rapid tuning of hyperparameters for DNN potential training, we developed a scalable multiobjective optimization evolutionary algorithm for supercomputers and tested it on the Summit system at the Oak Ridge Leadership Computing Facility (OLCF). The multiobjective approach is required due to the coupling of two learned values defining the potential: the energy and force. Using a large-scale implementation of the NSGA-II algorithm adapted for training DNN potentials, we discovered several optimal multiobjective combinations, including best choices of activation functions, learning rate scaling scheme, and pairing of the two radial cutoffs used in the three dimensional descriptor function.

Coletti, Mark↗
DOE OSTI2024

Orange carotenoid proteins: structural understanding of evolution and function

Cyanobacteria uniquely contain a primitive water-soluble carotenoprotein, the orange carotenoid protein (OCP). Nearly all extant cyanobacterial genomes contain genes for the OCP or its homologs, implying an evolutionary constraint for cyanobacteria to conserve its function. Genes encoding the OCP and its two constituent structural domains, the N-terminal domain, helical carotenoid proteins (HCPs), and its C-terminal domain, are found in the most basal lineages of extant cyanobacteria. These three carotenoproteins exemplify the importance of the protein for carotenoid properties, including protein dynamics, in response to environmental changes in facilitating a photoresponse and energy quenching. Furthermore, we review new structural insights for these carotenoproteins and situate the role of the protein in what is currently understood about their functions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Orbital Motion, Variability, and Masses in the T Tauri Triple System

We present results from adaptive optics imaging of the T Tauri triple system obtained at the Keck and Gemini Observatories in 2015−2019. We fit the orbital motion of T Tau Sb relative to Sa and model the astrometric motion of their center of mass relative to T Tau N. Using the distance measured by Gaia, we derived dynamical masses of M{sub Sa}=2.05±0.14 M {sub ⊙} and M {sub Sb} = 0.43 ± 0.06 M{sub ⊙}. The precision in the masses is expected to improve with continued observations that map the motion through a complete orbital period; this is particularly important as the system approaches periastron passage in 2023. Based on published properties and recent evolutionary tracks, we estimate a mass of ∼2 M {sub ⊙} for T Tau N, suggesting that T Tau N is similar in mass to T Tau Sa. Narrowband infrared photometry shows that T Tau N remained relatively constant between late 2017 and early 2019 with an average value of K = 5.54 ± 0.07 mag. Using T Tau N to calibrate relative flux measurements since 2015, we found that T Tau Sa varied dramatically between 7.0 and 8.8 mag in the K band over timescales of a few months, while T Tau Sb faded steadily from 8.5 to 11.1 mag in the K band. Over the 27 yr orbital period of the T Tau S binary, both components have shown 3–4 mag of variability in the K band, relative to T Tau N.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Nitrogen Deposition Weakens Soil Carbon Control of Nitrogen Dynamics Across the Contiguous United States

ABSTRACT Anthropogenic nitrogen (N) deposition is unequally distributed across space and time, with inputs to terrestrial ecosystems impacted by industry regulations and variations in human activity. Soil carbon (C) content normally controls the fraction of mineralized N that is nitrified ( ƒ nitrified ), affecting N bioavailability for plants and microbes. However, it is unknown whether N deposition has modified the relationships among soil C, net N mineralization, and net nitrification. To test whether N deposition alters the relationship between soil C and net N transformations, we collected soils from coniferous and deciduous forests, grasslands, and residential yards in 14 regions across the contiguous United States that vary in N deposition rates. We quantified rates of net nitrification and N mineralization, soil chemistry (soil C, N, and pH), and microbial biomass and function (as beta‐glucosidase (BG) and N ‐acetylglucosaminidase (NAG) activity) across these regions. Following expectations, soil C was a driver of ƒ nitrified across regions, whereby increasing soil C resulted in a decline in net nitrification and ƒ nitrified . The ƒ nitrified value increased with lower microbial enzymatic investment in N acquisition (increasing BG:NAG ratio) and lower active microbial biomass, providing some evidence that heterotrophic microbial N demand controls the ammonium pool for nitrifiers. However, higher total N deposition increased ƒ nitrified , including for high soil C sites predicted to have low ƒ nitrified , which decreased the role of soil C as a predictor of ƒ nitrified . Notably, the drop in contemporary atmospheric N deposition rates during the 2020 COVID‐19 pandemic did not weaken the effect of N deposition on relationships between soil C and ƒ nitrified . Our results suggest that N deposition can disrupt the relationship between soil C and net N transformations, with this change potentially explained by weaker microbial competition for N. Therefore, past N inputs and soil C should be used together to predict N dynamics across terrestrial ecosystems.

Nieland, Matthew A. [Stockbridge School of Agricul↗
DOE OSTI2021

Cross-Feeding of a Toxic Metabolite in a Synthetic Lignocellulose-Degrading Microbial Community

The recalcitrance of complex organic polymers such as lignocellulose is one of the major obstacles to sustainable energy production from plant biomass, and the generation of toxic intermediates can negatively impact the efficiency of microbial lignocellulose degradation. Here, we describe the development of a model microbial consortium for studying lignocellulose degradation, with the specific goal of mitigating the production of the toxin formaldehyde during the breakdown of methoxylated aromatic compounds. Included are Pseudomonas putida, a lignin degrader; Cellulomonas fimi, a cellulose degrader; and sometimes Yarrowia lipolytica, an oleaginous yeast. Unique to our system is the inclusion of Methylorubrum extorquens, a methylotroph capable of using formaldehyde for growth. We developed a defined minimal “Model Lignocellulose” growth medium for reproducible coculture experiments. We demonstrated that the formaldehyde produced by P. putida growing on vanillic acid can exceed the minimum inhibitory concentration for C. fimi, and, furthermore, that the presence of M. extorquens lowers those concentrations. We also uncovered unexpected ecological dynamics, including resource competition, and interspecies differences in growth requirements and toxin sensitivities. Finally, we introduced the possibility for a mutualistic interaction between C. fimi and M. extorquens through metabolite exchange. This study lays the foundation to enable future work incorporating metabolomic analysis and modeling, genetic engineering, and laboratory evolution, on a model system that is appropriate both for fundamental eco-evolutionary studies and for the optimization of efficiency and yield in microbially-mediated biomass transformation.

Methylorubrum extorquens↗
DOE OSTI2025

What are grana in chloroplasts of vascular plants good for?

All plants and green algae contain stacked grana thylakoid membranes in their chloroplasts, underscoring an evolutionary pressure to maintain this unique structural feature. In addition, numerous studies have demonstrated that particular lateral and vertical dimensions of grana facilitate the function, regulation and repair of the photosynthetic machinery responsible for energy conversion. In this review, we present an updated overview of our understanding concerning the structure of grana thylakoids, the forces that contribute to their formation and their architectural dynamics. After establishing the structural foundation, we continue by reviewing the implications of grana formation on light harvesting, electron transport and protein maintenance in the thylakoid membranes of vascular plants. Regarding light harvesting, we discuss the implications of grana formation on energy spillover, macromolecular crowding, lateral versus vertical excitation energy transfer, and state transition. With respect to electron transport, we summarize recent findings regarding the functional connectivity of spatially separated photosystems facilitated by grana formation through small mobile electron carriers. We also explore how the dynamic responses of grana architecture regulate electron transport. Finally, we address how grana formation contributes to the organization of protein repair and biogenesis within thylakoid membranes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

An evolutionary algorithm for designing microbial communities via environmental modification

Despite a growing understanding of how environmental composition affects microbial communities, it remains difficult to apply this knowledge to the rational design of synthetic multispecies consortia. This is because natural microbial communities can harbour thousands of different organisms and environmental substrates, making up a vast combinatorial space that precludes exhaustive experimental testing and computational prediction. Here, we present a method based on the combination of machine learning and metabolic modelling that selects optimal environmental compositions to produce target community phenotypes. In this framework, dynamic flux balance analysis is used to model the growth of a community in candidate environments. A genetic algorithm is then used to evaluate the behaviour of the community relative to a target phenotype, and subsequently adjust the environment to allow the organisms to approach this target. We apply this iterative process to thousands of in silico communities of varying sizes, showing how it can rapidly identify environments that yield desired taxonomic compositions and patterns of metabolic exchange. Moreover, this combination of approaches produces testable predictions for the assembly of experimental microbial communities with specific properties and can facilitate rational environmental design processes for complex microbiomes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

The final piece of the Triangle of U: Evolution of the tetraploid Brassica carinata genome

Abstract Ethiopian mustard (Brassica carinata) is an ancient crop with remarkable stress resilience and a desirable seed fatty acid profile for biofuel uses. Brassica carinata is one of six Brassica species that share three major genomes from three diploid species (AA, BB, and CC) that spontaneously hybridized in a pairwise manner to form three allotetraploid species (AABB, AACC, and BBCC). Of the genomes of these species, that of B. carinata is the least understood. Here, we report a chromosome scale 1.31-Gbp genome assembly with 156.9-fold sequencing coverage for B. carinata, completing the reference genomes comprising the classic Triangle of U, a classical theory of the evolutionary relationships among these six species. Our assembly provides insights into the hybridization event that led to the current B. carinata genome and the genomic features that gave rise to the superior agronomic traits of B. carinata. Notably, we identified an expansion of transcription factor networks and agronomically important gene families. Completion of the Triangle of U comparative genomics platform has allowed us to examine the dynamics of polyploid evolution and the role of subgenome dominance in the domestication and continuing agronomic improvement of B. carinata and other Brassica species.

Biochemistry & Molecular Biology↗
DOE OSTI2022

How do soil microbes shape ecosystem biogeochemistry in the context of global change?

Earth system models disagree on the future of global soil carbon C, with projections ranging from losses of 70 Pg to gains of 250 Pg by the year 2100 (Todd-Brown et al., 2014). This disagreement is due, in part, to the high degree of uncertainty in the mechanisms that control soil C, the largest dynamic pool of organic C on Earth. It remains unclear how climate change, rising atmospheric CO2 levels, and other environmental changes will combine to affect global soil C. As key engineers of biogeochemical cycling, microbes play a crucial role in soil C responses to these environmental changes. Yet, the path toward representing microbes in soil models remains uncertain. Furthermore, we address four burning questions relevant for modelling microbial ecological, evolutionary, and biogeochemical processes in soils: (1) What is the role of soil microbes in biogeochemical cycling? (2) Does microbial community composition matter for soil carbon cycling? (3) Which ecological and evolutionary processes contribute to functional changes? (4) How should we model microbial eco-evolutionary mechanisms in soil C models?

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Structure and sequence evolution in the pennycress ( Thlaspi arvense ) pangenome

Eukaryotic genomes harbor many forms of variation, including nucleotide diversity and structural polymorphisms, which experience natural selection and contribute to genome evolution and biodiversity. Harnessing this variation for agriculture hinges on our ability to detect, quantify, catalog, and deploy genetic diversity. Here, we explore seven complete genomes of the emerging biofuel crop pennycress ( Thlaspi arvense ) drawn from across the species' current genetic diversity to catalog variation in genome structure and content. Across this new pangenome resource, we find contrasting evolutionary modes in different genomic zones. Gene-poor, repeat-rich pericentromeric regions experience frequent rearrangements, including repeated centromere repositioning. By contrast, conserved gene-dense chromosome arms maintain large-scale synteny across accessions even in fast-evolving NOD-like receptor immune genes, where microsynteny breaks down across species, but gene cluster positioning macrosynteny is maintained. Our findings highlight that multiple elements of the genome experience dynamic evolution that conserves functional content on the chromosome scale but allows repositioning and presence–absence variation on a local scale. This diversity is invisible to classical reference-based strategies and highlights the strength and utility of pangenomic resources. These results provide a valuable case study of rapid genomic structural evolution within a species and powerful resources for crop development in an emerging biofuel crop.

Thlaspi arvense↗